Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of
identifying the precise boundary of every cell in an image. To address this problem we …

The development of deep segmentation models for computational pathology (CPath) can
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …

Deep learning has emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …

Biomedical image analysis is fundamental for biomedical discovery. Holistic image analysis
comprises interdependent subtasks such as segmentation, detection and recognition, which …

Detecting various types of cells in and around the tumor matrix holds a special significance
in characterizing the tumor micro-environment for cancer prognostication and research …

The success of training computer-vision models heavily relies on the support of large-scale,
real-world images with annotations. Yet such an annotation-ready dataset is difficult to …

Graph-based methods have been extensively applied to whole slide histopathology image
(WSI) analysis due to the advantage of modeling the spatial relationships among different …

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images
are important clinical tasks and crucial for a wide range of applications. However, it is a …